wehub-resource-sync
73 lines
2.4 KiB
C++
73 lines
2.4 KiB
C++
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <functional>
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#include <numeric>
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#include "paddle/common/flags.h"
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#include "test/cpp/inference/api/trt_test_helper.h"
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namespace paddle_infer {
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TEST(PredictorPool, use_gpu) {
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std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
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Config config;
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config.EnableUseGpu(100, 0);
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config.SetModel(model_dir);
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config.EnableTensorRtEngine();
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config.Exp_DisableTensorRtOPs({"fc"});
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config.EnableTensorRtDLA(0);
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services::PredictorPool pred_pool(config, 1);
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auto predictor = pred_pool.Retrieve(0);
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auto input_names = predictor->GetInputNames();
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auto input_t = predictor->GetInputHandle(input_names[0]);
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std::vector<int> in_shape = {1, 3, 224, 224};
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int in_num = std::accumulate(
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in_shape.begin(), in_shape.end(), 1, std::multiplies<int>());
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std::vector<float> input(in_num, 0);
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input_t->Reshape(in_shape);
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input_t->CopyFromCpu(input.data());
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predictor->Run();
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}
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TEST(PredictorPool, use_trt_cuda_graph) {
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std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
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Config config;
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config.EnableUseGpu(100, 0);
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config.SetModel(model_dir);
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config.EnableTensorRtEngine(
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1 << 20, 1, 3, PrecisionType::kFloat32, false, false, true);
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config.Exp_DisableTensorRtOPs({"fc"});
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config.EnableTensorRtDLA(0);
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services::PredictorPool pred_pool(config, 1);
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auto predictor = pred_pool.Retrieve(0);
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auto input_names = predictor->GetInputNames();
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auto input_t = predictor->GetInputHandle(input_names[0]);
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std::vector<int> in_shape = {1, 3, 224, 224};
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int in_num = std::accumulate(
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in_shape.begin(), in_shape.end(), 1, std::multiplies<int>());
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std::vector<float> input(in_num, 0);
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input_t->Reshape(in_shape);
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input_t->CopyFromCpu(input.data());
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predictor->Run();
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}
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} // namespace paddle_infer
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